Integrating geographic and space data in urban CBRN models - Riskaware

Integrating geographic and space data in urban CBRN models

By Russell Mills

Riskaware’s UrbanAware platform provides powerful geographic and spatial intelligence, through our Geographic and Environmental Database Information System (GEDIS) tool, to inform comprehensive urban CBRN/HazMat models and support effective incident response.

Currently, Riskaware are working with external organisations to develop GEDIS with enriched performance, a broader scope of capabilities, and enhanced usability, in addition to future plans to integrate live data into GEDIS for greater use on the tactical edge.

Geographic and Environmental Database Information System (GEDIS)

GEDIS is available through our HASP Suite of CBRN incident modelling tools. This solution acts as the underlying database, providing in-depth geographic intelligence about the surrounding area to support rapid downwind hazard assessment support.

Empowered by information from GEDIS, our users have the data they need to accurately model how an airborne contaminant will travel through and impact any given environment, considering key factors such as urban topography, air flow, and population density.

GEDIS is also integrated into our UrbanAware platform, which is an end-to-end CBRN / HazMat incident modelling and information management solution used by emergency services, government organisations, and military.

There are three primary elements included in GEDIS: 

  • Geographic Data: Vector data about urban and rural areas across the globe.
  • Spatial Analysis: A spatially indexed database which provides important contextual information to discrete geographic data.
  • Raster Grids: Classifies and identifies more detailed characteristics of different areas and environments.

All of this data is available through the GEDIS tool, alongside additional features such as population data which are designed to provide additional context to incident models about the impact of an airborne hazard on public safety. GEDIS will provide the right data for the right time and merge all relevant intelligence into a single, easy to understand output.

DOWNLOAD THE GEDIS DATASHEET FOR MORE INFORMATION

Ongoing US Projects

Lawrence Berkeley National Laboratory is leading a project for the creation of integrated indoor and outdoor CBRN modelling for airborne contaminants in urban environments. As part of this work, Riskaware developed workflows to import open-source urban data into GEDIS, for use in the Hazard Prediction and Analysis Capability (HPAC).

Riskaware have further been working with DTRA (The Defense Threat Reduction Agency) to apply customer feedback to the development of our GEDIS tool, and subsequently implement both design improvements and speed updates.

Future Developments

Alongside the capability, design and performance improvements are currently underway. Riskaware plan to adapt GEDIS in ways which further support situational awareness during live and evolving incidents.

Plans involve redesigning GEDIS to function on the server-side, which would enable a compact version of the tool to be deployed onto various handheld devices, compared to its current deployment which requires a traditional computer. Not only is this less computationally expensive, as it requires reduced processing power, but it would enable users to use GEDIS on the scene of an incident. Bringing GEDIS to the tactical edge in this way would allow more up-to-date data from the location to be exploited for incident models, and empower users, such as first responders, with extremely timely and relevant outputs for real-time decision making.

Riskaware also hope to integrate live data streams into GEDIS in the future, allowing information from the scene of a CBRN/HazMat event, for example imagery taken by a drone, to be directly input into GEDIS and immediately turned into insight.

Get in touch to learn more about our work on indoor and outdoor modelling or how GEDIS can empower you with greater location-based intelligence during a CBRN incident.

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